On Physics-Informed Neural Network Control for Power Electronics

TL;DR

This paper presents a novel physics-informed neural network control approach that combines data-driven and model-driven methods to improve the stability and robustness of power electronics in uncertain grid environments.

Contribution

It introduces a new methodology integrating PINNs with control strategies for power electronics, enhancing accuracy and robustness under uncertainties.

Findings

Demonstrates improved stability in power electronics control.

Shows robustness under operational uncertainties.

Validates effectiveness through experimental results.

Abstract

Considering the growing necessity for precise modeling of power electronics amidst operational and environmental uncertainties, this paper introduces an innovative methodology that ingeniously combines model-driven and data-driven approaches to enhance the stability of power electronics interacting with grid-forming microgrids. By employing the physics-informed neural network (PINN) as a foundation, this strategy merges robust data-fitting capabilities with fundamental physical principles, thereby constructing an accurate system model. By this means, it significantly enhances the ability to understand and replicate the dynamics of power electronics systems under complex working conditions. Moreover, by incorporating advanced learning-based control methods, the proposed method is enabled to make precise predictions and implement the satisfactory control laws even under serious uncertain conditions. Experimental validation demonstrates the effectiveness and robustness of the proposed approach, highlighting its substantial potential in addressing prevalent uncertainties in controlling modern power electronics systems.